Browsing by Author "NOBARI, Sadegh"

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    Discretionary Social Network Data Revelation with a User-Centric Utility Guarantee
    (2012-08-08) YI, Song; KARRAS, Panagiotis; NOBARI, Sadegh; CHELIOTIS, Giorgos; XUE, Mingqiang; BRESSAN, Stéphane
    The proliferation of online social networks has created intense interest in studying the nature of such networks and revealing network information of interest to the end user. At the same time, the revelation of such data raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on what constitutes a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: instead of starting out with a privacy objective, we define a utility guarantee, in terms of certain graph connectivity properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the whole network graph, in which that user as well as distant connections and affliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real-world data, we demonstrate that our method preserves graph properties of interest (e.g., clustering coefficient, shortest path length, diameter, radius) more successfully than methods that randomly distort the graph to an equal extent, while it withstands structural attacks proposed in the literature.
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    Edit Distance between XML and Probabilistic XML Documents
    (2011-06-03) TANG, Ruiming; WU, Huayu; NOBARI, Sadegh; BRESSAN, Stephane
    We propose an efficient algorithm for computing of the edit distance between an XML document and a probabilistic XML document. Probabilistic XML is a hierarchical data model capturing uncertainty of both value and structure. It is suitable to many modern applications such as information extraction, scientific data management and data integration. The computation of similarity is an essential building block for the comparison, alignment, clustering and classification of data in these applications. Several algorithms exist for measuring the structural similarity between XML documents among themselves or XML documents and XML document type definitions and schemas. The new challenge in efficiently computing the similarity between an XML document and a probabilistic XML document is the multiplicity of the possible worlds that a probabilistic XML document represents. In this paper, we devise and discuss algorithms for computing the similarity between an XML document and a probabilistic XML document. We empirically and comparatively evaluate their performance. In the absence of established corpora and benchmarks for probabilistic XML, we also propose and use random probabilistic XML models together with the associated random generation algorithms.